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Related Concept Videos

Imaging Studies for Cardiovascular System I:Echocardiography01:17

Imaging Studies for Cardiovascular System I:Echocardiography

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Cardiac imaging studies encompass a wide range of noninvasive and minimally invasive techniques designed to visualize the heart's structure and function in detail. One such technique is echocardiography, which uses high-frequency ultrasound waves to produce detailed images of the heart, known as echocardiograms.
Indications: Echocardiography is utilized to diagnose heart failure, valve disorders, and myocardial infarction. It also assesses cardiac structures' size, shape, and motion,...
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Imaging Studies for Cardiovascular System II:Types of Echocardiography01:20

Imaging Studies for Cardiovascular System II:Types of Echocardiography

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Echocardiography plays a role in assessing cardiac health and detecting heart conditions, with various types providing critical insights for diagnosis and treatment.
Types of Echocardiography
Transthoracic Echocardiography (TTE)
TTE is the most common type of echocardiogram which involves placing a transducer on the patient's chest, emitting sound waves to create heart images. TTE is invaluable for evaluating the heart's size, structure, and motion, making it particularly useful for...
221

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Related Experiment Video

Updated: May 30, 2025

Evaluation of Left Ventricular Structure and Function using 3D Echocardiography
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Urgency Prediction for Medical Laboratory Tests Through Optimal Sparse Decision Tree: Case Study With

Yiqun Jiang1, Qing Li2, Yu-Li Huang1

  • 1Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, United States.

JMIR AI
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Summary

This study developed an interpretable machine learning model to prioritize echocardiogram appointments, improving patient scheduling and identifying key factors for urgency. The model offers valuable insights for efficient healthcare resource allocation.

Keywords:
appointment schedulingechocardiogramhealth care managementinterpretable machine learningurgency prediction

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Area of Science:

  • Health Informatics
  • Machine Learning in Healthcare
  • Precision Medicine

Background:

  • Laboratory tests are crucial for precision medicine but face accessibility challenges.
  • Echocardiograms are vital but have high demand and scheduling complexities.
  • Limited research exists on optimizing echocardiogram appointment scheduling.

Purpose of the Study:

  • Develop an interpretable machine learning model to determine echocardiogram appointment urgency.
  • Prioritize patient scheduling for echocardiograms efficiently.
  • Identify key patient attributes influencing echocardiogram appointment prioritization.

Main Methods:

  • Utilized a large-scale real-world echocardiogram appointment dataset (34,293 records).
  • Employed the Optimal Sparse Decision Tree (OSDT), a state-of-the-art interpretable machine learning algorithm.
  • Analyzed administrative data, referral diagnoses, and patient conditions.

Main Results:

  • The OSDT model showed satisfactory performance, outperforming baseline models.
  • Achieved F1-score of 36.18% (1.7% improvement) and F2-score of 28.18% (0.79% improvement).
  • Extracted decision rules from the OSDT model provided medical insights for identifying urgent patients.

Conclusions:

  • The interpretable OSDT model demonstrated effective predictive performance for prioritizing echocardiogram urgency.
  • Decision rules derived from the model align with established medical knowledge.
  • The approach can be extended to prioritize other laboratory test appointments using electronic health record data.